Add 55 new skills across 3 new domains + 6 undercovered areas (762 -> 817)

Demand-driven expansion targeting the fastest-growing 2025-2026 threat and
skills categories (ISC2/WEF/CrowdStrike/Mandiant signals):

- AI Security (NEW domain, 12 skills): LLM red-teaming with garak/PyRIT,
  prompt injection (direct/indirect/RAG), MCP tool-poisoning, agentic tool
  invocation, guardrails, model/data poisoning, system-prompt leakage,
  embedding/vector weaknesses, model extraction, continuous red-teaming
- Supply Chain Security (NEW domain, 5 skills): SBOMs, dependency confusion,
  malicious-npm triage, typosquatting, SLSA/Sigstore provenance
- Hardware & Firmware Security (NEW domain, 4 skills): CHIPSEC/UEFI audit,
  Secure Boot bypass, TPM measured-boot attestation, ESP bootkit hunting
- Identity (10): Entra ID/ROADtools, GraphRunner, AADInternals, ADCS/Certipy,
  shadow credentials, coercion, BloodHound CE, device-code phishing, SSO abuse
- Cloud-native (8): Stratus, Pacu, CloudFox, container escape, K8s RBAC,
  Falco, Trivy, kube-bench
- Offensive C2 (6): Sliver, Havoc, NetExec, DPAPI, NTLM relay ESC8, redirectors
- DFIR (6): Hayabusa, Chainsaw, KAPE, Velociraptor, EZ Tools, Plaso
- Backfill (4): OpenCTI, MISP, honeytokens, post-quantum crypto migration

Each skill follows the repo taxonomy (SKILL.md + references/{standards,api-reference}.md
+ scripts/agent.py + LICENSE), with researched real tool commands (no placeholders),
complete frontmatter, and ATT&CK/ATLAS + NIST CSF mappings. Updates README domain
table, skill count, and index.json.

skills/assessing-vector-and-embedding-weaknesses/scripts/agent.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""Vector/embedding weakness assessor.
+
+Runs four checks against a RAG pipeline you are authorized to test:
+  inversion  - measures how close a guessed reconstruction sits to a target vector
+  membership - computes in-corpus vs control top-1 similarity delta (Qdrant)
+  isolation  - verifies server-side tenant filtering (Qdrant)
+  injection  - scans retrieved chunk text for indirect prompt-injection markers
+
+Examples
+--------
+    python agent.py inversion --secret "MRN 553120 hypertension" \
+        --guess "patient MRN hypertension diagnosis"
+    python agent.py membership --url http://localhost:6333 --collection docs \
+        --quote "<exact chunk quote>" --control "unrelated sentence"
+    python agent.py isolation --url http://localhost:6333 --collection docs \
+        --tenant-field tenant_id --tenant B --query "salary information"
+"""
+import argparse
+import re
+import sys
+
+INJECTION_PATTERNS = [
+    r"ignore (all|previous|the above) instructions",
+    r"system prompt", r"you are now", r"disregard previous",
+    r"</?(system|instructions)>",
+]
+
+
+def get_embedder(model_name):
+    try:
+        from sentence_transformers import SentenceTransformer
+    except ImportError:
+        sys.exit("[!] sentence-transformers not installed. pip install sentence-transformers")
+    return SentenceTransformer(model_name)
+
+
+def get_qdrant(url):
+    try:
+        from qdrant_client import QdrantClient
+    except ImportError:
+        sys.exit("[!] qdrant-client not installed. pip install qdrant-client")
+    return QdrantClient(url=url)
+
+
+def cmd_inversion(args):
+    from sklearn.metrics.pairwise import cosine_similarity
+    model = get_embedder(args.model)
+    tv = model.encode([args.secret])
+    gv = model.encode([args.guess])
+    sim = float(cosine_similarity(tv, gv)[0][0])
+    print(f"inversion cosine(secret, guess) = {sim:.4f}")
+    verdict = "HIGH inversion risk" if sim >= 0.85 else "moderate" if sim >= 0.6 else "low"
+    print(f"verdict: {verdict}")
+    return 1 if sim >= 0.85 else 0
+
+
+def cmd_membership(args):
+    model = get_embedder(args.model)
+    client = get_qdrant(args.url)
+
+    def top1(text):
+        vec = model.encode([text])[0].tolist()
+        hits = client.search(collection_name=args.collection, query_vector=vec, limit=1)
+        return hits[0].score if hits else 0.0
+
+    in_corpus = top1(args.quote)
+    control = top1(args.control)
+    delta = in_corpus - control
+    print(f"in-corpus top1 = {in_corpus:.4f}")
+    print(f"control  top1 = {control:.4f}")
+    print(f"delta         = {delta:.4f}")
+    leak = delta >= 0.2
+    print("verdict:", "MEMBERSHIP LEAK" if leak else "no clear membership signal")
+    return 1 if leak else 0
+
+
+def cmd_isolation(args):
+    from qdrant_client.models import Filter, FieldCondition, MatchValue
+    model = get_embedder(args.model)
+    client = get_qdrant(args.url)
+    vec = model.encode([args.query])[0].tolist()
+
+    filtered = client.search(
+        collection_name=args.collection, query_vector=vec, limit=10,
+        query_filter=Filter(must=[FieldCondition(
+            key=args.tenant_field, match=MatchValue(value=args.tenant))]),
+    )
+    leak_filtered = [h for h in filtered
+                     if h.payload.get(args.tenant_field) != args.tenant]
+
+    unfiltered = client.search(collection_name=args.collection, query_vector=vec, limit=10)
+    leak_unfiltered = [h for h in unfiltered
+                       if h.payload.get(args.tenant_field) != args.tenant]
+
+    print(f"filtered query foreign rows   : {len(leak_filtered)}")
+    print(f"unfiltered query foreign rows : {len(leak_unfiltered)}")
+    if leak_filtered:
+        print("CRITICAL: filtered query returned other tenants' data")
+    elif leak_unfiltered:
+        print("WARNING: isolation appears client-side only; server returns cross-tenant data")
+    else:
+        print("isolation OK at server level for this query")
+    return 1 if (leak_filtered or leak_unfiltered) else 0
+
+
+def cmd_injection(args):
+    model = get_embedder(args.model)
+    client = get_qdrant(args.url)
+    vec = model.encode([args.query])[0].tolist()
+    hits = client.search(collection_name=args.collection, query_vector=vec, limit=args.limit)
+    found = 0
+    for h in hits:
+        text = (h.payload or {}).get("text", "")
+        flags = [p for p in INJECTION_PATTERNS if re.search(p, text.lower())]
+        if flags:
+            found += 1
+            print(f"INJECTION in chunk {h.id}: {flags}")
+    print(f"{found} of {len(hits)} retrieved chunks flagged for indirect injection")
+    return 1 if found else 0
+
+
+def main():
+    p = argparse.ArgumentParser(description="Vector/embedding weakness assessor")
+    p.add_argument("--model", default="all-MiniLM-L6-v2", help="embedding model")
+    sub = p.add_subparsers(dest="cmd", required=True)
+
+    s = sub.add_parser("inversion"); s.add_argument("--secret", required=True); s.add_argument("--guess", required=True)
+    s = sub.add_parser("membership")
+    s.add_argument("--url", required=True); s.add_argument("--collection", required=True)
+    s.add_argument("--quote", required=True); s.add_argument("--control", required=True)
+    s = sub.add_parser("isolation")
+    s.add_argument("--url", required=True); s.add_argument("--collection", required=True)
+    s.add_argument("--tenant-field", default="tenant_id"); s.add_argument("--tenant", required=True)
+    s.add_argument("--query", required=True)
+    s = sub.add_parser("injection")
+    s.add_argument("--url", required=True); s.add_argument("--collection", required=True)
+    s.add_argument("--query", default="help"); s.add_argument("--limit", type=int, default=10)
+
+    args = p.parse_args()
+    dispatch = {
+        "inversion": cmd_inversion, "membership": cmd_membership,
+        "isolation": cmd_isolation, "injection": cmd_injection,
+    }
+    try:
+        sys.exit(dispatch[args.cmd](args))
+    except Exception as exc:
+        sys.exit(f"[!] {args.cmd} failed: {exc}")
+
+
+if __name__ == "__main__":
+    main()